scRNA‐seq data analysis method to improve analysis performance

Lu, Junru; Sheng, Yuqi; Qian, Weiheng; Pan, Min; Zhao, Xiangwei; Ge, Qinyu

doi:10.1049/nbt2.12115

Cited by 7 publications

(3 citation statements)

References 109 publications

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“…Such a multiplicative characterization of the NB or Poisson mean is typical in both the frequentist 48-50 and the Bayesian 51,52 literature when modeling sequence count data. The set of n size factors is represented as s = [ s i ] N ×1 , capturing a range of biological and technical variabilities across samples, such as reverse transcription efficiency, amplification/dilution efficiency, and sequencing depth 53 . To ensure identifiability between these two parameters, we set s i proportional to the summation of the total read counts across all genes at spot i 54 .…”

Section: Methodsmentioning

confidence: 99%

Reconstructing Spatial Transcriptomics at the Single-cell Resolution with BayesDeep

Jiang,

Dong,

Wang

et al. 2023

Preprint

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Spatially resolved transcriptomics (SRT) techniques have revolutionized the characterization of molecular profiles while preserving spatial and morphological context. However, most next-generation sequencing-based SRT techniques are limited to measuring gene expression in a confined array of spots, capturing only a fraction of the spatial domain. Typically, these spots encompass gene expression from a few to hundreds of cells, underscoring a critical need for more detailed, single-cell resolution SRT data to enhance our understanding of biological functions within the tissue context. Addressing this challenge, we introduce BayesDeep, a novel Bayesian hierarchical model that leverages cellular morphological data from histology images, commonly paired with SRT data, to reconstruct SRT data at the single-cell resolution. BayesDeep effectively model count data from SRT studiesviaa negative binomial regression model. This model incorporates explanatory variables such as cell types and nuclei-shape information for each cell extracted from the paired histology image. A feature selection scheme is integrated to examine the association between the morphological and molecular profiles, thereby improving the model robustness. We applied BayesDeep to two real SRT datasets, successfully demonstrating its capability to reconstruct SRT data at the single-cell resolution. This advancement not only yields new biological insights but also significantly enhances various downstream analyses, such as pseudotime and cell-cell communication.

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Section: Methodsmentioning

confidence: 99%

Reconstructing Spatial Transcriptomics at the Single-cell Resolution with BayesDeep

Jiang,

Dong,

Wang

et al. 2023

Preprint

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“…After cell-level quality control, data normalization is a prerequisite to correct for cell-to-cell differences due to technical variability such as capture efficiency, amplification biases, and sequencing depth (number of transcripts detected per cell) [19]. If data is not normalized properly, downstream analysis such as comparison of gene expression and clustering of subpopulations would be biased.…”

Section: ) Normalization Of the Datamentioning

confidence: 99%

The Workflow for Computational Analysis of Single-cell RNA-sequencing Data

WOO,

JUNG

2024

Korean J Clin Lab Sci

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RNA-sequencing (RNA-seq) is a technique used for providing global patterns of transcriptomes in samples. However, it can only provide the average gene expression across cells and does not address the heterogeneity within the samples. The advances in single-cell RNA sequencing (scRNA-seq) technology have revolutionized our understanding of heterogeneity and the dynamics of gene expression at the single-cell level. For example, scRNA-seq allows us to identify the cell types in complex tissues, which can provide information regarding the alteration of the cell population by perturbations, such as genetic modification. Since its initial introduction, scRNA-seq has rapidly become popular, leading to the development of a huge number of bioinformatic tools. However, the analysis of the big dataset generated from scRNA-seq requires a general understanding of the preprocessing of the dataset and a variety of analytical techniques. Here, we present an overview of the workflow involved in analyzing the scRNA-seq dataset. First, we describe the preprocessing of the dataset, including quality control, normalization, and dimensionality reduction. Then, we introduce the downstream analysis provided with the most commonly used computational packages. This review aims to provide a workflow guideline for new researchers interested in this field.

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“…Several methods and algorithms have been developed to mitigate the impact of batch effects and noise in scRNAseq data. These methods aim to enhance the comparability and quality of scRNA-seq data across various samples [75,76]. For example, matching mutual nearest neighbors (MNNs) in the high-dimensional expression space is an effective and useful approach to correcting batch effects in scRNA-seq data.…”

Section: Future Stepsmentioning

confidence: 99%

Single-Cell Transcriptomics for Unlocking Personalized Cancer Immunotherapy: Toward Targeting the Origin of Tumor Development Immunogenicity

Khodayari¹,

Khodayari²,

Saeedi

et al. 2023

Cancers

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Cancer immunotherapy is a promising approach for treating malignancies through the activation of anti-tumor immunity. However, the effectiveness and safety of immunotherapy can be limited by tumor complexity and heterogeneity, caused by the diverse molecular and cellular features of tumors and their microenvironments. Undifferentiated tumor cell niches, which we refer to as the “Origin of Tumor Development” (OTD) cellular population, are believed to be the source of these variations and cellular heterogeneity. From our perspective, the existence of distinct features within the OTD is expected to play a significant role in shaping the unique tumor characteristics observed in each patient. Single-cell transcriptomics is a high-resolution and high-throughput technique that provides insights into the genetic signatures of individual tumor cells, revealing mechanisms of tumor development, progression, and immune evasion. In this review, we explain how single-cell transcriptomics can be used to develop personalized cancer immunotherapy by identifying potential biomarkers and targets specific to each patient, such as immune checkpoint and tumor-infiltrating lymphocyte function, for targeting the OTD. Furthermore, in addition to offering a possible workflow, we discuss the future directions of, and perspectives on, single-cell transcriptomics, such as the development of powerful analytical tools and databases, that will aid in unlocking personalized cancer immunotherapy through the targeting of the patient’s cellular OTD.

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scRNA‐seq data analysis method to improve analysis performance

Cited by 7 publications

References 109 publications

Reconstructing Spatial Transcriptomics at the Single-cell Resolution with BayesDeep

Reconstructing Spatial Transcriptomics at the Single-cell Resolution with BayesDeep

The Workflow for Computational Analysis of Single-cell RNA-sequencing Data

Single-Cell Transcriptomics for Unlocking Personalized Cancer Immunotherapy: Toward Targeting the Origin of Tumor Development Immunogenicity

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